@article {1195,
	title = {HPG pore: an efficient and scalable framework for nanopore sequencing data.},
	journal = {BMC bioinformatics},
	volume = {17},
	year = {2016},
	month = {2016},
	pages = {107},
	abstract = {BACKGROUND: The use of nanopore technologies is expected to spread in the future because they are portable and can sequence long fragments of DNA molecules without prior amplification. The first nanopore sequencer available, the MinION{\texttrademark} from Oxford Nanopore Technologies, is a USB-connected, portable device that allows real-time DNA analysis. In addition, other new instruments are expected to be released soon, which promise to outperform the current short-read technologies in terms of throughput. Despite the flood of data expected from this technology, the data analysis solutions currently available are only designed to manage small projects and are not scalable. RESULTS: Here we present HPG Pore, a toolkit for exploring and analysing nanopore sequencing data. HPG Pore can run on both individual computers and in the Hadoop distributed computing framework, which allows easy scale-up to manage the large amounts of data expected to result from extensive use of nanopore technologies in the future. CONCLUSIONS: HPG Pore allows for virtually unlimited sequencing data scalability, thus guaranteeing its continued management in near future scenarios. HPG Pore is available in GitHub at http://github.com/opencb/hpg-pore .},
	keywords = {hadoop, HPC, nanopore, NGS},
	issn = {1471-2105},
	doi = {10.1186/s12859-016-0966-0},
	url = {http://www.biomedcentral.com/1471-2105/17/107},
	author = {T{\'a}rraga, Joaqu{\'\i}n and Gallego, Asunci{\'o}n and Arnau, Vicente and Medina, Ignacio and Dopazo, Joaquin}
}
@article {1162,
	title = {Concurrent and Accurate Short Read Mapping on Multicore Processors.},
	journal = {IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM},
	volume = {12},
	year = {2015},
	month = {2015 Sep-Oct},
	pages = {995-1007},
	abstract = {We introduce a parallel aligner with a work-flow organization for fast and accurate mapping of RNA sequences on servers equipped with multicore processors. Our software, [Formula: see text] ([Formula: see text] is an open-source application. The software is available at http://www.opencb.org, exploits a suffix array to rapidly map a large fraction of the RNA fragments (reads), as well as leverages the accuracy of the Smith-Waterman algorithm to deal with conflictive reads. The aligner is enhanced with a careful strategy to detect splice junctions based on an adaptive division of RNA reads into small segments (or seeds), which are then mapped onto a number of candidate alignment locations, providing crucial information for the successful alignment of the complete reads. The experimental results on a platform with Intel multicore technology report the parallel performance of [Formula: see text], on RNA reads of 100-400 nucleotides, which excels in execution time/sensitivity to state-of-the-art aligners such as TopHat 2+Bowtie 2, MapSplice, and STAR.},
	keywords = {HPC, NGS, short real mapping},
	issn = {1557-9964},
	doi = {10.1109/TCBB.2015.2392077},
	url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=\&arnumber=7010005},
	author = {Martinez, Hector and T{\'a}rraga, Joaqu{\'\i}n and Medina, Ignacio and Barrachina, Sergio and Castillo, Maribel and Dopazo, Joaquin and Quintana-Orti, Enrique S}
}
@article {1149,
	title = {A Parallel and Sensitive Software Tool for Methylation Analysis on Multicore Platforms.},
	journal = {Bioinformatics (Oxford, England)},
	volume = {31},
	number = {19},
	year = {2015},
	month = {2015 Jun 10},
	pages = {3130-3138},
	abstract = {MOTIVATION: DNA methylation analysis suffers from very long processing time, since the advent of Next-Generation Sequencers (NGS) has shifted the bottleneck of genomic studies from the sequencers that obtain the DNA samples to the software that performs the analysis of these samples. The existing software for methylation analysis does not seem to scale efficiently neither with the size of the dataset nor with the length of the reads to be analyzed. Since it is expected that the sequencers will provide longer and longer reads in the near future, efficient and scalable methylation software should be developed. RESULTS: We present a new software tool, called HPG-Methyl, which efficiently maps bisulfite sequencing reads on DNA, analyzing DNA methylation. The strategy used by this software consists of leveraging the speed of the Burrows-Wheeler Transform to map a large number of DNA fragments (reads) rapidly, as well as the accuracy of the Smith-Waterman algorithm, which is exclusively employed to deal with the most ambiguous and shortest reads. Experimental results on platforms with Intel multicore processors show that HPGMethyl significantly outperforms in both execution time and sensitivity state-of-the-art software such as Bismark, BS-Seeker or BSMAP, particularly for long bisulfite reads. AVAILABILITY: Software in the form of C libraries and functions, together with instructions to compile and execute this software. Available by sftp to anonymous@clariano.uv.es (password "anonymous"). CONTACT: Juan.Orduna@uv.es.},
	keywords = {BS-seq, HPC, methylation, NGS},
	issn = {1367-4811},
	doi = {10.1093/bioinformatics/btv357},
	url = {http://bioinformatics.oxfordjournals.org/content/31/19/3130.long},
	author = {T{\'a}rraga, Joaqu{\'\i}n and P{\'e}rez, Mariano and Ordu{\~n}a, Juan M and Duato, Jos{\'e} and Medina, Ignacio and Joaqu{\'\i}n Dopazo}
}
